Interacting Object Tracking in Crowded Urban Areas

Chieh-Chih Wang, Tzu-Chien Lo and Shao-Wen Yang

Department of Computer Science and Information Engineering

National Taiwan University, Taipei, Taiwan

Email: bobwang@ntu.edu.tw,{bright,any}@robotics.csie.ntu.edu.tw

Abstract— Tracking in crowded urban areas is a dauntingtask. High crowdedness causes challenging data associationproblems. Different motion patterns from a wide variety ofmoving objects make motion modeling difficult. Accompany-ing with traditional motion modeling techniques, this paperintroduces a scene interaction model and a neighboring objectinteraction model to respectively take long-term and short-terminteractions between the tracked objects and its surroundingsinto account. With the use of the interaction models, anomalousactivity recognition is accomplished easily. In addition, move-stop hypothesis tracking is applied to deal with move-stop-move maneuvers. All these approaches are seamlessly inter-graded under the variable-structure multiple-model estimationframework. The proposed approaches have been demonstratedusing data from a laser scanner mounted on the PAL1 robotat a crowded intersection. Interacting pedestrians, bicycles,motorcycles, cars and trucks are successfully tracked in difficultsituations with occlusion.

I. INTRODUCTION

Scene understanding is a key prerequisite for making a

robot truly autonomous. Establishing the spatial and temporal

relationships among the robot, stationary objects and moving

objects serves as the basis for scene understanding. In [1],

[2], we presented the theory and algorithms to solve the

Simultaneous Localization, Mapping and Moving Object

Tracking (or SLAMMOT) problem. The experimental results

from a ground vehicle at high speeds in crowded urban areas

demonstrated the feasibility of SLAMMOT. A wide variety

of moving object in crowded urban areas are detected and

tracked successfully. The stationary and moving object maps

are built incrementally. However, we assumed that the robot

and moving objects move independently of each other to

reduce the complexity of SLAMMOT enormously. This inde-

pendence assumption may be unrealistic in human inhabited

environments such as crowded urban areas, shopping malls

and railway stations. These environments contain a large

number of constraints which affect the motions of moving

objects. Targets interact both with other moving objects and

their surrounding environments. Interactions among moving

objects and stationary objects should be of interest for higher

level scene understanding.

Without explicitly detecting and modeling interactions, a

number of approaches address the filtering and data associa-

tion issues of interacting object tracking. Veeraraghavan et al.

[3] addressed a multilevel tracking approach using Kalman

filter for tracking pedestrians and vehicles using cameras at

intersections. Zhao and Shibasaki [4] accomplished tracking

multiple pedestrians using multiple laser scanners where

Fig. 1. Left: The PAL1 robot. Right: the robot collecting data at a crowdedintersection near National Taiwan University.

pedestrians’ feet are detected and the pattern of the rhythmic

swing feet are tracked.

To properly address the interacting object tracking prob-

lem, detecting and modeling of interactions are critical.

Oliver et al. [5] described a coupled hidden Markov

model framework for recognizing human interactions such

as follow, approach+talk+continue, and change direc-

tion+approach+talk+continue in a pedestrian plaza. Panan-

gadan et al. [6] uses a simple distance-based method for

detecting interactions among people crossing a courtyard.

Bruce and Gordon [7] proposed a statistical learning method

to model interactions between a target and the surrounding

environment for better motion prediction. In [8], Khan et

al. proposed a Markov chain Monte Carlo (MCMC)-based

particle filter to track interacting ants in which interactions

are modeled through a Markov random field motion prior.

In this paper, both short-term and long-term interactions

are defined and integrated into the tracking process. The

long-term interactions are modeled with the use of the

stationary and moving object map built by SLAMMOT. In

addition to the interaction between the tracked object and

the stationary objects [9][7], behavior patterns of previous

dynamic objects are taken into account. A simple abnormal

activity recognition can be accomplished with this approach.

The short-term interactions are modeled with the use of

neighboring object tracking. As moving objects in urban

areas obey the same traffic laws, strong interactions between

the tracked object and the neighboring objects should always

exist to avoid accidents. The tracking process is fused

with the tracked object’s own motion and the motion of

the neighboring objects. This short-term interactions deal

with occlusion issues effectively, provide better move-stop

2007 IEEE International Conference onRobotics and AutomationRoma, Italy, 10-14 April 2007

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switching prediction and achieve better tracking performance

and accuracy than traditional methods. Instead of designing

more complex motion models, our novel approach is to

simply update the target’s state estimate using the virtual

measurement generated by the interaction models in the

update stage of filtering.

As move-stop-move maneuvers often occur in crowded

urban areas, a soft switching model-set algorithm of the

variable structure multiple-model estimator [10], or the

move-stop hypothesis tracking approach [2], is applied.

The interaction models are seamlessly integrated with this

theoretically solid multiple-model estimator framework. The

feasibility of the proposed approaches are demonstrated

using data collected from a SICK laser scanner mounted on

the PAL1 robot at a crowded intersection as shown in Figure

1. The visual images are only for visualization in this work.

II. BACKGROUND

In this section, we review the theoretical foundations of

tracking, describe the variable structure multiple-model esti-

mator and introduce the mathematical notation for describing

the proposed approaches.

A. Bayesian Tracking

The tracking problem can be solved with the mechanism

of Bayesian approaches such as Kalman filter and Particle

filter. Assuming that the true motion mode of a target is

known, we can get a simple form of moving object tracking.

p(xk | Zk) (1)

where xk is the true state of the moving object at time k,

and Zk = {z1,z2, · · · ,zk} is the perception measurement set

leading up to time k. According to the Bayes’ theorem and

the Markov assumption, Equation 1 is derived and expressed

as:

p(xk | Zk) ∝ p(zk | xk)

∫

p(xk | xk−1)p(xk−1 | Zk−1)dxk−1 (2)

where p(xk−1 | Zk−1) is the posterior probability at time k−1,

p(xk | Zk) is the posterior probability at time k, p(xk | xk−1)is the motion model and p(zk | xk) is the measurement or

perception model.

B. Motion Modeling

The true motion mode is often unavailable in many appli-

cations. Online motion modeling is needed. Equation 1 can

be modified and formalized in the probabilistic form as:

p(xk,sk | Zk) ∝ p(zk | xk,sk) (3)

∑sk−1

∫

p(xk,sk | xk−1,sk−1)p(xk−1,sk−1 | Zk−1)dxk−1

where sk is the true motion mode of the moving object at

time k.

Motion Modeling, or estimation of structural parameters

of a system, is called system identification in the control

literature and learning in the artificial intelligence literature.

From a theoretical point of view, motion modeling is as im-

portant as perception/measurement modeling in Bayesian ap-

proaches. From a practical point of view, without reasonably

good motion models, the predictions may be unreasonable

and cause serious problems in data association and inference.

For online motion modeling, using more models is not

necessarily the optimal solution. Additionally, it increases

computational complexity considerably. Li [11] provided a

theoretical proof that even the optimal use of motion models

does not guarantee better tracking performance.

Use of a fixed set of models is not the only option

for multiple model based tracking approaches. A variable

structure (VS) can be used in multiple model approaches

[12]. By selecting the most probable model subset, estimation

performance can be improved. However, this requires more

complicated computation procedures. Not only motion but

also other types of information or constraints can be selected

and added to the model set. In [13], terrain conditions are

used as constraint models and are added to the model set to

improve performance of ground target tracking via a variable

structure interacting multiple model (VS-IMM) algorithm.

The details of the variable structure multiple-model es-

timation and the related algorithms are available in [12].

Although our primary contribution is to take both stationary

and moving object interactions into account in the up-

date stage instead of in the predication stage, move-stop-

move maneuvers are taken care under the variable structure

multiple-model estimation framework.

C. Move-Stop Hypothesis Tracking

The move-stop hypothesis tracker follows the variable

structure multiple-model estimation, which has two motion

model set, the move model set and the stop model set. The

model sets of the move-stop hypothesis tracker can therefore

be expressed as:

Q = {q(move),q(stop)} (4)

where q(move) can consist of common motion models such as

the constant-velocity (CV) model, the constant-acceleration

(CA) model, the constant-turn (CT) model. Here the inter-

acting multiple model (IMM) approach [14] is applied to

integrate all motion models. q(stop) is the stationary process

model described in Chapter 4.4 of [2].

In practice, the minimum detection velocity (MDV) can

be obtained by taking account of the modeled uncertainty

sources. For objects whose velocity estimates from the IMM

algorithm with the moving models are larger than this

minimum detection velocity, the objects are unlikely to be

stationary and the IMM algorithm with the moving models

should perform well.

For objects whose velocity estimates are less than this

minimum detection velocity, tracking should be done with

great caution. Instead of adding the stationary process model

to the model set, move-stop hypothesis tracking is applied

where the move hypothesis and the stop hypothesis are

inferred separately.

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For move hypothesis inference, tracking is done via the

IMM algorithm. For stop hypothesis inference, the stationary

process model is used to verify if the system is a stationary

process at the moment with a short time period of mea-

surements. The covariances from the move hypothesis and

the stop hypothesis are compared. The hypothesis with more

certain estimates will take over the tracking process.

Figure 2 demonstrates the performance of move-stop

hypothesis tracking. It is clear that move-stop hypothesis

tracking correctly tracked a move-stop-move maneuver of the

tracked motorcycle. Without move-stop hypothesis tracking,

the estimate diverged.

III. INTERACTION-AIDED TRACKING

In this section, we describe a scene interaction model to

represent the long-term interactions and a neighboring object

interaction model to represent the short-term interactions.

Instead of using complex motion modeling techniques, the

interaction models produce virtual measurements to aid

tracking via the update stage of filtering.

A. Scene Interaction Model

The scene interaction model is designed to represent the

long-term interactions between the target and its surround-

ings. As the temporal and spatial information is embedded in

the stationary and moving object map built by SLAMMOT,

the scene interaction model uses the map to predict/constrain

the possible future motion and pose of the target.

1) Modeling: The environment map built by SLAMMOT

previously contains only the occupancy information of sta-

tionary and moving objects. Here the map is stored with

additional information such as speed and direction of moving

objects. Motion directions of tracked targets are discretized

into one of nine canonical values, i.e., eight for canonical

directions and one for stationary objects. The stationary

mode consists of one bin and each of the eight directions

consists of b bins which is given as:

β (v) =

{ ⌊

vinterval

⌋

for 0 ≤ v < b · interval

b−1 for v ≥ b · interval(5)

where v is the speed of the occupied object and interval is

a pre-determined constant. In our experiments, interval is

10 km/hr and b is 8. Each bin records the occurrence count

of each speed value for each direction. Figure 3 illustrates

the information contained by a single grid of the built map.

Figure 4 shows the built maps in which only the most

observed direction of a grid is shown.

In our scenarios, the urban traffics contain strong long-

term interactions because of traffic laws. Therefore, the

SLAMMOT maps are automatically generated and main-

tained according to these behavior patterns. The behavior

patterns are classified with the use of motion directions

of all moving objects in the scene. The scene interaction

model will use the corresponding map to predict/constrian

the tracked object’s motion. Figure 4 shows the SLAMMOT

map according to three different behavior patterns.

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X (meter)

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(a) Tracking result of the whole scene. Rectangles denote trackedmoving objects. The rectangle with a bold trajectory denotes thetracked motorcycle. The bold rectangle is enlarged in (c) and (d).

(b) The bold rectangle is the tracked motorcycle

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Fig. 2. Move-stop hypothesis tracking: in (c) and (d), ×s are themeasurements. The distributions of the estimates are shown by 1σ ellipse.The estimates are at the center of the ellipses which are not shown forclarity.

E

S

N

W

E

S

N

W

Fig. 3. The SLAMMOT map contains information of occupancy, speedand direction. Two examples are shown in which different motion patternsare embedded into the map. Left is from a road lane and right is from acrosswalk.

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(a) Three different behavior patterns of an urban scene. Only the most observed motion direction of a grid is shown by an arrow inside the grid. Blackgrids are belonging to stationary objects. White grids are unobserved or unoccupied areas. The robot is at the origin (0,0) of the map.

(b) The photos illustrate different behavior patterns of the scene.

Fig. 4. The SLAMMOT map.

2) Prediction: With the use of the SLAMMOT maps and

the scene behavior pattern recognition results, we predict the

possible motions of a tracked object using a sampling-based

method.

Let Ek be a set containing the state vectors of these

randomly generated samples e[i]k at time k where Ek =

⋃

i e[i]k .

The samples are weighted with respect to the SLAMMOT

map. If the grid occupied by the sample belonging to

stationary objects, the sample’s weight w[i]k is set to zero.

If not, the weight w[i]k of the sample e

[i]k is proportional to the

probability of the motion specified by e[i]k at the occupied

grid.

Given the samples and their corresponding weights, the

effect of the the scene interaction model is represent by the

mean and covariance (z(scene)) of these weighted samples as

shown in Figure 5. The SLAMMOT process integrates the

previous real measurements into the stationary and moving

object map. The scene interaction model uses the map to

generate the virtual measurement about the target to predict

or constrain the target’s future motion. The rest of fusion is

straightforward. The target state is simply updated with this

virtual measurement.

With the use of the SLAMMOT map, the scene interaction

model may only provide a more uncertain estimate than

the prediction from the target’s motion models. However,

this method effectively takes the constraints/interactions from

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Fig. 5. Sampling-based prediction from the scene interaction model. Left:a motorcycle passing a narrow gate. Right: a car moving near a medianstrip.

both stationary and moving objects into account. Figure 6

demonstrates the capability of tracking in a occlusion situa-

tion with the use of the scene interaction model. Figure 6(a)

shows the tracking result using the IMM model. While the

target is occluded, the estimate is predicted without update.

The target state estimate uncertainty increases quickly and

finally diverges. False data association results in failure in

tracking of its surrounding objects. Figure 6(c) shows inter-

acting object tracking with the proposed scene interaction

model. The information contained in the SLAMMOT map

is employed to predict the target’s motion. The occluded ob-

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(a) Tracking using the IMM model.

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(b) The enlargement of (a).

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(c) Tracking with the use of the sceneinteraction model

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(e) Camera images at scan 1152

(f) Camera images at scan 1164

Fig. 6. The scene interaction model: ×s are the measurements. Thedistributions of the estimates are shown by 1σ ellipse.

ject’s state was correctly tracked. The feasibility of tracking

is evaluated using the visual images from the onboard camera

images as depicted in Figure 6(e). We later will show that

this approach also provides a simple way to detect abnormal

events.

B. Neighboring Object Interaction Model

The neighboring object interaction model is designed to

represent the short-term or immediate interactions between

the target and its neighboring objects. As addressed in [5],

there are several different types of short-term interactions.

In this paper, we only deal with the follow interaction

which frequently happens in crowded urban areas. According

to the follow interaction assumption, we simply consider

interactions between the target and the neighboring objects in

front of the tracked object. Note that the proposed framework

allows more different types of short-term interactions via

multiple hypothesis tracking approaches. However, it is a

challenging problem to detect with which objects the target

is currently interacting.

With the use of the same virtual measurement technique

proposed in the scene interaction model, the neighboring

object interaction model generates a corresponding virtual

measurement z(neighbor) according to the neighboring object’s

motion to predict/constraint the target’s motion.

Given the estimate xk of the target at time k, let yk be the

state estimate of the nearest neighboring object in front of the

target. The virtual measurement from the neighboring object

interaction model and its corresponding covariance can be

computed as:

zj(neighbor)

k = Hj

k

(

xjk +

(

Fj

k − I)

yjk

)

∀m j ∈ Q (6)

Rj(neighbor)

k = Hj

k

(

Fj

k Tj

k FjT

k +Qjk

)

∀m j ∈ Q (7)

where Fj

k is the process model under the motion model m j

of the tracked target at time k, I denotes the identity matrix,

Hj

k is the measurement model under the motion model m j

at time k, Tj

k is the covariance of the neighboring object yk

under the motion model m j at time k, and Qjk is the motion

noise model under the motion model m j of the tracked target

at time k.

The estimate of the tracked object’s pose is then updated

with this virtual measurement straightforwardly. Figure 7

demonstrates that tracking using the neighboring object inter-

action model perform well in the occlusion situation. Figure

7(a) shows the tracking result using the IMM model. While

the target is occluded, the estimate is predicted without up-

date. The target state estimate uncertainty increases quickly

which results in wrong data association in this crowded

scene. The track is lost in this case. Figure 7(c) shows

interacting object tracking with the use of neighboring object

interaction model. The motion of the target’s neighboring

object is applied to predict the target’s motion. The occluded

object’s state was correctly tracked. The correctness of track-

ing is evaluated using the visual images from the onboard

cameras, as shown in 7(e).

IV. EXPERIMENTAL RESULTS

A couple of interacting object tracking results have been

shown in the previous sections. Figures 8 demonstrates the

tracking results of pedestrians, bicycles, motorcycles, cars

and trucks.

Anomalous event detection can be easily accomplished

using the interaction models. Here anomalous events are

defined as that objects act differently from predictions of

the scene interaction model and the neighboring interaction

model. Figure 9 demonstrates an example of abnormal event

recognition in which a bicyclist disobeyed the traffic laws. As

there is no other object around this bicycle, the neighboring

object interaction model was not activated but the scene

interaction model quickly showed that the bicycle’s motion is

very different from the prediction. The attached video shows

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Fig. 7. The neighboring object interaction model: ×s are the measurements.The distributions of the estimates are shown by 1σ ellipse.

the whole sequence of this abnormal event recognition and

interacting object tracking results.

Another example of anomalous event detection is shown

in Figure 10. A car broke down and stopped in the road.

As the car was stationary, the neighboring object interaction

model was not activated but the scene interaction model

quickly showed that the car’s motion is very different from

the prediction. Traffic accidents can be easily detected by

employing the proposed interaction models.

V. CONCLUSION

Tracking a wide variety of interacting moving objects

in crowded urban areas is difficult. Based on our previous

contribution to SLAMMOT, the primary contribution of

this paper is to introduce the scene interaction model and

the neighboring object interaction model for taking both

long-term and short-term interactions into account. These

interaction models and move-stop hypothesis tracking are

seamlessly integrated using the variable-structure multiple

model estimation framework. Fusion of these interaction

models is simply accomplished using the virtual measure-

ments in the update stage of filtering. The ample experi-

mental results using data from a laser scanner collected at a

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Fig. 8. Experimental Tracking results of pedestrians, bicycles, motorcycles,and cars.

crowded urban intersection have demonstrated the feasibility

and effectiveness of the proposed algorithms.

Future work will further add more short-term interactions

such as passing to deal with more complicated scenarios,

and collect more data to analyze the statistical properties

of the scene interaction models in different urban areas. It

would be of interest to study the effectiveness of the proposed

algorithms in areas with weaker interactions such as offices

and homes.

VI. ACKNOWLEDGMENTS

This work was partially supported by grants from Taiwan

NSC (#94-2218-E-002-077, #94-2218-E-002-075, #95-2221-

E-002-433), Quanta Computer, Australia’s CSIRO, and Intel.

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